检索结果-内蒙古大学图书馆

Efficient Monkeypox Detection in Skin Lesions Using Pre-Trained Deep Learning Technique

Efficient Monkeypox Detection in Skin Lesions Using Pre-Trai...

2024 International Conference on artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Vandana Sharma, Chetna Chahal, Ansh Daryal, Neeti Chitkara University Institute of Engineering and Technology Punjab Rajpura India Mukand Lal National College Kurukshetra University Yamuna nagar India

ISBN: (纸本)9798331517953

A medical emergency was declared by the World Health Organization and the Department of Health when the monkeypox virus gained prominence during the COVID-19 pandemic. Traditional classification systems struggle to accurately identify skin disorders because viruses that cause them often have similar skin appearances. Concerns about security and privacy are often raised when sharing sensitive medical data. A workable solution to the issues related to medical data categorization and illness detection is provided by the combination of Federated Learning and deep neural networks. The proposed technique recommends using pre-trained Deep-Learning technique in a federated learning technique to identify the virus that infects human skin. The Kaggle and open-source datasets were both used. In order to develop a global technique, the research sought to collaborate with various local datasets while maintaining privacy concerns. The proposed technique is simultaneously computing the categorical accuracy level, F1-score micro, and F1-score macro. The effectiveness of the proposed technique is validated by comparing the procured results with the existing state-of-art techniques. © 2024 IEEE.

关键词： Deep Learning Federated Learning image processing Monkeypox

来源：评论

学校读者我要写书评

暂无评论

Household Waste Classification Algorithms Based on Various Convolutional neural networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

引用

21st International Computer Conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Cancer Cell Detection and Classification from Digital Whole Slide image 5th

Cancer Cell Detection and Classification from Digital Whole ...

引用

5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Gavade, Anil B. Nerli, Rajendra B. Ghagane, Shridhar Gavade, Priyanka A. Bhagavatula, Venkata Siva Prasad Department of E&C KLS Gogte Institute of Technology Karnataka Belagavi India Karnataka Belagavi India Department of Biotechnology KAHER’s Dr. Prabhakar Kore Basic Science Research Center V. K. Institute of Dental Sciences Campus Karnataka Belagavi India Department of Computer Science and Engineering KLE Tech University Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belagavi India Hyderabad Medtronic India

ISBN: (纸本)9789811968792

The World Health Organisation has identified cancer as one of the foremost causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatment option for the patient. Pathological data have huge tumour information that can be used to diagnose cancer. Digitizing pathological data into images and its analysis using Deep learning applications will be a significant contribution to clinical testing. Due to advancements in technology, artificial intelligence (AI) and digital pathology can now be combined allowing for image-based diagnosis. This study uses residual networks (ResNet-50) and convolutional neural network (CNN), which is pre-trained on imageNet dataset to train and categories lung histopathology images into non-cancerous, lung adenocarcinoma, and lung squamous cell carcinoma delivering an accuracy of 98.9%. Experimentation results show that the ResNet-50 model delivers finer classification results when compared to state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： artificial intelligence Classification Convolutional neural network Deep learning Graphic processing unit Histopathological image Residual networks

来源：评论

学校读者我要写书评

暂无评论

image Quality Assessment: Edge Based Entropy features estimation using Soft Computing Techniques

引用

Materials Today: Proceedings 2022年 56卷 3261-3271页

作者： Wasson, Vikas Kaur, Bikrampal CSE I.K.G Punjab Technical University Punjab Jalandhar India Department of CSE CEC Landran I.K.G Punjab Technical University Punjab Jalandhar India

With the always expanding interest for image handling based applications, the requirement for image Quality Assessment (IQA) techniques for proficient and dependable assessment of picture quality in concurrence with human quality decisions has similarly taken off at each phase of image processing. The limits of customary IQA strategies as far as having the option to work just under a pre-characterized set of filtering algorithms confines the effectiveness of methods in explicit degradations has roused the investigation of IQA dependent on Soft Computing metaheuristic techniques, which are equipped for choosing the features shrewdly. A swarm based meta heuristic technique known as Grey Wolf Optimization (GWO) has been utilized, which impersonates the conduct of wolf packs to encompass and chase their prey. This paper presents a basic and direct strategy for artificially assessing computerized images by a solitary parameter, entropy. The benchmark dataset Colourlab image Database: image Quality (CID:IQ) is employed for the extensive Matlab investigations. Outcomes are addressed graphically to approve the viability of GWO algorithm over others. GWO evidently surmounts artificial neural networks (ANN) based optimization algorithm in terms of entropy. These meliorations are likely to propel the future researchers to prefer the soft computing techniques for use in different image handling areas including horticultural sciences, medication, cryptography, and so on, for evaluating their images in pre-processing phases for improved outcomes. © 2022

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Optimization of laser processing parameters through automated data acquisition and artificial neural networks

引用

JOURNAL OF LASER applications 2021年第4期33卷 1-6页

作者： Vo, Cameron Zhou, Boyang Yu, Xiaoming Univ Cent Florida Coll Opt & Photon CREOL 4304 Scorpius St Orlando FL 32816 USA

Finding the optimal parameters in laser processing applications can be time-consuming given the large parameter space and various sources of error. This problem is exacerbated by day-to-day variation in laser beam characteristics and a large variety of materials that need to be processed. The ideal laser processing system should be "smart, " meaning that it can sense changes in the environment, make proper adjustment, and predict parameters for new materials. As a step toward this goal, we propose a method to efficiently extract the areas of a large number of laser-induced damages in situ using an automated data acquisition system that can control laser parameters, motorized stage movement, image capturing/processing, and feature extraction. The damage areas are extracted and compared with direct measurements. Damage areas are fed into an artificial neural network (ANN) for prediction. Various ANN structures and training functions are tested to create the optimal ANN for prediction. ANN predictions were found to be capable enough to accurately model and optimize the laser processing parameters that were investigated. With the capability of collecting a large amount of usable data in a short period of time, this acquisition system can be used to train sophisticated ANNs for complicated tasks such as quality control and failure prediction.

关键词： Laser materials processing artificial neural network Ultrafast laser Fused silica

来源：评论

学校读者我要写书评

暂无评论

4th International Conference on artificial Intelligence in China, AIC 2022

4th International Conference on Artificial Intelligence in C...

引用

4th International Conference on artificial Intelligence in China, AIC 2022

ISBN: (纸本)9789819912551

The proceedings contain 49 papers. The special focus in this conference is on artificial Intelligence in China. The topics include: Research on Helmet Detection Algorithm Based on Improved YOLOv5s;label Embedding Based Scoring Method for Secondary School Essays;Research on Wheat Ears Detection Method Based on Improved YOLOv5;Aircraft Target Detection Algorithm Based on Improved YOLOv5s;a Survey in Virtual image Generation Based on Generative Adversarial networks;research on the Service for the Disabled in Interior Design and Product Design Based on artificial Intelligence;application and Prospect of artificial Intelligence image Analysis Technology in Natural Resources Survey;application and Technical Analysis of Computer Vision Technology in Natural Resource Survey;research on Natural Language processing Technology in Construction Project Management;review of Generative Adversarial networks in Object Detection;a Review of the Application of Convolutional neural networks in Object Detection;optimal Deployment of Large-Scale Wireless Sensor Network Based on Topological Potential Adaptive Graph Clustering;INSL: Text2SQL Generation Based on Inverse Normalized Schema Linking;the Research on Prediction for Financial Distress in Car Company Listed Combining Financial Indicators and Text Data;frame Interpolation for Weather Radar Data;a Hybrid neural Network for Music Generation Using Frequency Domain Data;Prediction Model for Inclusive Finance Development Considering the Impact of COVID-19: The Case of China;certifiable Optimal Visual Pose Estimation for Space applications;Automotive Manufacturing Revenue Prediction Using Financial and Comment Sentiment Data Based on CNN Model;exploring the Application of SketchUp and Twinmotion in Building Design Planning;Deep Learning-Enhanced ICA Algorithm for Sub-Gaussian Blind Source Separation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potency of neural networks: A Thorough Examination of Deep Learning Techniques

Exploring the Potency of Neural Networks: A Thorough Examina...

引用

Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Manisha Tyagi Panipat Institute of Engineering & Technology Samalkha Panipat Haryana India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

Deep learning stands as a revolutionary paradigm in artificial intelligence (AI), demonstrating its prowess across diverse areas, such as image recognition, natural language processing, and autonomous systems. This review article offers a comprehensive exploration of various deep learning techniques, shedding light on their distinctive features, applications, and recent advancements. From classic architectures to cutting-edge innovations, we delve into the diverse landscape of neural networks that have reshaped the field of artificial intelligence.

关键词： Deep learning Technological innovation image recognition Reviews Navigation neural networks Computer architecture Propulsion Natural language processing artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Aircraft Recognition Using Convolutional neural Network

Aircraft Recognition Using Convolutional Neural Network

引用

2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023

作者： Wan Zulkipli, Nabilah Chuah, Joong Huang Lim, Marcus Guozong Chai, Grace Mei Ting Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur52200 Malaysia Institute for Manufacturing Department of Engineering University of Cambridge CambridgeCB3 0FS United Kingdom Faculty of Engineering and Physical Sciences University of Southampton Malaysia Johor Iskandar Puteri79200 Malaysia

ISBN: (纸本)9781839539596

Satellite image object-recognition offers detailed location representation where ground-based sampling is limited or inaccessible, such as high sea or in the mountains. However, an accurate and effective method is required to ensure the precision of the information generated due to small, ambiguous objects against the background. Having an automatic object detection system would greatly speed up satellite image recognition and find extensive applications in defense, transportation, agriculture, and emergency relief. With the advancement of artificial neural networks, the technology has seen applications in various fields from image processing to speech synthesis. We aimed to develop a neural network-based commercial aircraft detection system on satellite images at major airports. We proposed a R-CNN-based aircraft recognition system where CNN is utilized to extract features of aircraft before passing the feature maps to the binary classifier. We trained the model with 2100 images of satellite images containing aircraft which are segmented to produce positive and negative instances and validated on 900 images. The system is equipped to detect and localize the position of the aircraft in an image while registering excellent accuracy. However, as the number of aircraft in a frame increases, slight deterioration in the model performance is observed. © The Institution of Engineering & Technology 2023.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction 2

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Yogaraja, C.A. Priyanka, C. Priyadharshini, S.P. Babu, J. Jagan Rajagopal, S. Poorani, S. Ramco Institute of Technology Rajapalayam India School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus India St. Joseph's College of Engineering OMR Chennai119 India Department of ECE R.M.D Engineering College Kavaraipettai India Department of IT National Engineering College Kovilppati India Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350383867

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

IoT-Driven image Recognition for Microplastic Analysis in Water Systems using Convolutional neural networks 2

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

引用

2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Hasan, M D Ashfaqul Balasubadra, K. Vadivel, G. Arunfred, N. Ishwarya, M.V. Murugan, S. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India R.M.D. Engineering College Kavaraipettai Department of Information Technology Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional neural networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics' influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management. © 2024 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：